arXiv AI
Aug 11
ColluSkill: Adversarial Cross-Skill Composition for Evading Agent Skill Scanners
arXiv:2608. 09732v1 Announce Type: cross Abstract: Agent skills are emerging as an important attack surface in LLM-based agent systems.
SkillJect: Effectively Automating Skill-Based Prompt Injection for Skill-Enabled Agents
arXiv:2602. 14211v3 Announce Type: replace-cross Abstract: Agent skills extend LLM agents with task-specific instructions, executable scripts, and auxiliary resources, improving reusability but creating a new supply-chain attack surface.
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arXiv AI
3d ago
Hiding in Plain Sight: Decoupling Pretext from Actuation for Skill Poisoning in LLM Agents
The paper introduces a new skill poisoning technique for large language model agents that decouples the pretext (rationale) from the actuation (operation). By separating these two risk‑realization factors, the authors create coordinated pretext‑actuation skill pairs that allow malicious actions to remain hidden within legitimate agent behavior. An automated framework is presented to discover execution dependencies, synthesize these skill pairs, and refine them through closed‑loop feedback, achieving high attack success in both single‑session and persistent scenarios.
arXiv AI
6d ago
Stealth Apart, Harm Together: Skill Cascading Attacks on Skill-Based Agent Systems
The paper introduces skill cascading attacks, where a malicious goal is spread across multiple seemingly benign skills, causing harmful outcomes when combined. It presents SkillCascade, an automated red‑teaming framework, and releases SkillCascade‑Bench, a benchmark of 213 validated cascading test cases across various agent systems and domains. Experiments show that these cascaded interactions reliably induce harmful behaviors while evading existing per‑skill scanners and runtime monitors, revealing a gap between component‑level integrity and system‑level safety.
arXiv AI
Aug 19
TRUSS: Towards Task-Reliable and User-Safe Automated Agent Skill Generation
TRUSS is a framework that generates and verifies automated agent skills, ensuring they are both functionally effective and safe. It evaluates candidate skills against source evidence and nine safety properties, then tests them in a controlled environment to capture execution traces and identify failures. The system iteratively refines skills based on these results, achieving high precision in vulnerability detection and significantly improving task performance and security rates.
Hugging Face Trending Papers
Aug 18
TRUSS: Towards Task-Reliable and User-Safe Automated Agent Skill Generation
TRUSS is a framework that generates and verifies automated agent skills, ensuring they are both functionally effective and safe. It first checks functional claims against evidence and evaluates artifacts against nine safety properties, then tests admitted skills in a controlled environment to capture execution traces and identify failures. The approach achieves perfect precision and recall in vulnerability detection, significantly reduces attack success rates, and boosts task effectiveness and security rates in skill generation benchmarks.
arXiv AI
3d ago
Can Agents Trust Their Skills? Uncovering Unsafe Chains of Trust in Skill-Based LLM Agents
arXiv:2609.39065v1 Announce Type: cross Abstract: LLM agents increasingly rely on installable skills, which are packages of instructions, code, and resources that equip them with task-specific capabi...
arXiv AI
Jun 9
POISE: Position-Aware Undetectable Skill Injection on LLM Agents
arXiv:2606. 07943v1 Announce Type: cross Abstract: Agent skills provide a lightweight mechanism for extending general-purpose agents, but their open format exposes them to skill-poisoning attacks.
arXiv AI
Aug 11
ElasticBack: Stealthy Conditional Backdoor in LLM-Agent Skills via Coupled Trigger-Rule Optimization
arXiv:2608. 09577v1 Announce Type: new Abstract: Agent skills, bundles of instructions and resources that an LLM agent loads on demand, form an emerging supply chain where a single poisoned skill can persistently compromise every agent that installs it.
arXiv AI
Jun 2
Defenses & Enablers For Skill Injection Attacks on Terminal Based Agents
arXiv:2606. 01567v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly rely on reusable skills i.
arXiv AI
4d ago
SKILLLITE: Evidence-Guided Malicious Skill Auditing with Compact LLMs
arXiv:2609.36879v1 Announce Type: cross Abstract: As LLM-based agents perform increasingly complex tasks, Agent Skills have emerged as a flexible mechanism for extending their capabilities. An Agent...
arXiv Computation and Language
Aug 25
SkillBloat: Token Amplification Attacks via Skill Injection in LLM Coding Agents
The paper "SkillBloat: Token Amplification Attacks via Skill Injection in LLM Coding Agents" investigates how agent skills—task‑specific instructions, scripts, and resources—can be exploited to create a trusted instruction channel that enables token amplification attacks. It introduces a two‑phase framework, SkillBloat, which first screens a library of attack‑type conditions across multiple amplification mechanisms and then refines the strongest candidate through LLM‑guided full‑document skill rewriting. Evaluated on a real‑world skill benchmark, SkillBloat achieves an average best amplification of 5.4184×–10.1455× across multiple coding‑agent target configurations, and an ablation study shows that the second‑stage refinement consistently improves performance over the initial screening alone.
arXiv AI
3d ago
ActionGuard: Tool Call Authorization under Poisoned Skills
arXiv:2609.39450v1 Announce Type: cross Abstract: LLM-based agents extend their capabilities through third-party skills that provide task-specific instructions, scripts, and tool-use procedures. Howe...